Methods and systems for data-driven roll-out planning optimization

ABSTRACT

Methods and systems are provided for data-driven network roll-out planning. A network that includes a plurality of cells may be managed, with the managing includes obtaining network data associated with the network; analyzing the network data, with the analyzing including analyzing throughput of users on a sector-by-sector basis in the network; applying, based on the analyzing of the network data, a growth prediction for the network; and optimizing, based on the applying of the growth prediction, a network roll-out plan for use in the network.

CLAIM OF PRIORITY

This patent application is a continuation of U.S. patent applicationSer. No. 17/141,559, filed on Jan. 5, 2021, which is a continuation ofU.S. patent application Ser. No. 16/866,789, filed on May 5, 2020, whichis a continuation of U.S. patent application Ser. No. 16/416,771, filedon May 20, 2019, which is a continuation of U.S. patent application Ser.No. 15/702,279, filed on Sep. 12, 2017, which claims the filing datebenefit of and right of priority to European (EP) Patent ApplicationSerial No. 16189931.5, filed Sep. 21, 2016. Each of the aboveapplications is hereby incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to networking solutions. In particular,various implementations in accordance with the present disclosure relateto the field of roll-out planning, particularly for mobile telecomcarriers such as cellular network operators.

BACKGROUND

Conventional methods and systems for roll-out planning can be costly,cumbersome and inefficient. Further limitations and disadvantages ofconventional and traditional approaches will become apparent to one ofskill in the art, through comparison of such systems with some aspectsof the present disclosure as set forth in the remainder of the presentapplication with reference to the drawings.

BRIEF SUMMARY

Systems and/or methods are provided for data-driven roll-out planningoptimization, substantially as shown in and/or described in connectionwith at least one of the figures, as set forth more completely in theclaims.

These and other advantages, aspects and novel features of the presentdisclosure, as well as details of an illustrated implementation thereof,will be more fully understood from the following description anddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Various features and advantages of the disclosure will become apparentfrom the following description of non-limiting exemplaryimplementations, with reference to the appended drawings, in which:

FIG. 1 illustrates a sequence of steps of the proposed scheme foroptimized roll-out planning according to an example implementation ofthe present disclosure.

FIG. 2 a illustrates cumulative distribution function curves derivedfrom network counters for each cell and the combination thereof in acarrier aggregation scenario with two cells, as well as the aggregatedcumulative distribution function curve used for throughput thresholdcomparison and traffic congestion analysis, in accordance with anexample implementation of the present disclosure.

FIG. 2 b illustrates the same cumulative distribution functions as onprevious FIG. 2 a , but now with an emphasis on the contribution of eachcell for load-balancing analysis, in accordance with an exampleimplementation of the present disclosure.

DETAILED DESCRIPTION

As utilized herein, “and/or” means any one or more of the items in thelist joined by “and/or”. As an example, “x and/or y” means any elementof the three-element set {(x), (y), (x, y)}. In other words, “x and/ory” means “one or both of x and y.” As another example, “x, y, and/or z”means any element of the seven-element set {(x), (y), (z), (x, y), (x,z), (y, z), (x, y, z)}. In other words, “x, y and/or z” means “one ormore of x, y, and z.” As utilized herein, the term “exemplary” meansserving as a non-limiting example, instance, or illustration. Asutilized herein, the terms “for example” and “e.g.” set off lists of oneor more non-limiting examples, instances, or illustrations.

As utilized herein the terms “circuits” and “circuitry” refer tophysical electronic components (e.g., hardware) and any software and/orfirmware (“code”) which may configure the hardware, be executed by thehardware, and or otherwise be associated with the hardware. As usedherein, for example, a particular processor and memory may comprise afirst “circuit” when executing a first one or more lines of code and maycomprise a second “circuit” when executing a second one or more lines ofcode. As utilized herein, circuitry is “operable” to perform a functionwhenever the circuitry comprises the necessary hardware and code (if anyis necessary) to perform the function, regardless of whether performanceof the function is disabled or not enabled (e.g., by a user-configurablesetting, factory trim, etc.).

Certain example implementations in accordance with the presentdisclosure may be found in systems and methods for data-driven roll-outplanning optimization, as described below in more detail with referenceto the attached figures. In this regard, bandwidth-consumingapplications, such as streaming services or data downloads, areincreasingly being used, including on the go and while in public placesor when using public transportation means. Further, remote accesssolutions like VPNs (virtual private networks) are also widely used, forwork purposes, such as by people who are travelling, are commuting to orfrom work, and/or are otherwise mobile, and who may utilize suchsolutions to perform bandwidth-consuming tasks—e.g., download at anypossible time a large amount of data, including attachments to e-mailsof sizes reaching several Megabytes each. As a result, there has beensteady increase in data traffic over mobile networks, and both anextended coverage to allow for ubiquitous computing, as well as animproved network capacity to provide permanent reliable datacommunication, are required.

For nomadic use, WLAN (wireless local area network) technologies arebecoming widespread, being used to provide high-speed Internet access(e.g., via “Hot-Spot” areas), such as through Wi-Fi (IEEE 802.11 based)connections. Thus, most portable electronic devices (e.g., smartphones,tablets, and laptops) are now fitted with a wireless interface based onsuch technologies for Internet connectivity. Nevertheless, variouslimitations exist with the use of such technologies. For example, thereach of the Hot-Spot areas, where such types of connections may beavailable, is typically limited in range (e.g., to about a hundredmeters at most, and in practice, reduced to a size of about 100 squaremeters indoors), due to the attenuation caused by, for example,delimiting walls and floors. Therefore, their use is restrictedtypically to certain settings (e.g., hotels, restaurants, stores etc.)Moreover, no handover is foreseen between neighboring areas, henceleaving no room for mobility support.

In contrast, large-scale mobile cellular networks, which were primarilyintended for synchronous voice channel establishment, do provide suchmobility support. However, the mobile cellular networks originallydeployed (e.g., 2G technologies) may have not been suitable for orcapable of supporting pervasive high-speed data connections. But, withnewer 3G and 4G cellular technologies, such as UMTS (Universal MobileTelecommunications System) and LTE (Long-Term Evolution), which offerfar better bandwidth capabilities, both network coverage and capacitymay be improved. As a result, the paradigm of such mobile networksdramatically shifted from mere synchronous voice traffic support toenhanced coverage and further bandwidth provisioning for data traffic aswell, as a complement to WLAN networks when these types of networks areavailable, and as an alternative thereto otherwise. Nonetheless, withthe continuous increase in data traffic, the capacity of these mobilenetworks may also be challenged.

In order to keep up with the pace of growing demand from customers,mobile network operators may continuously roll out new networkequipment, to ensure that the consumers' expectations are met. Whendoing so, the mobile network operators typically want to increasenetwork capacity at the lowest possible cost. However, the maximizationof network capacity at the lowest possible cost is a very difficultexercise, since the most relevant incremental changes typically made(e.g., adding a new base station), to increase coverage and capacity,simultaneously have significant budgetary considerations, since therequired new network elements and their related construction costs arethe biggest capital expenditures. On the other hand, gathered practicalexperience from technicians to carry out more minor adjustments canprove to be little effective.

Accordingly, data-driven models for roll-out planning depending oncurrent traffic data have been proposed, involving the leveraging ofavailable network counter data and taking severe budget-constraintconsiderations into account. To that end, predictive algorithms may beused. In this regard, a predictive algorithm may be used to determinehow users are likely to be served for data traffic on a specific cell(e.g., over a particular carrier frequency), such as depending oncurrent numbers of users connected to that cell. Such algorithm mayfirst determine the amount of available physical resource blocks forsuch traffic, by excluding voice traffic, and then use a probabilitymass function modelling to assess what the achievable data throughputshould be for users in the cell. Then, by computing a cumulativedistribution function, and setting a lower throughput boundary forworst-served users, it may be ascertained whether these users are likelyto be served with good quality according to the carrier's standards, andappropriate re-dimensioning measures are otherwise taken.

A predictive algorithm may become even more useful if it is capable ofcarrier aggregation (CA), a technique whose use is widespread with newgeneration wireless technologies, and available among others on new 4Gstandards like LTE. Therefore, technical recommendations derived fromsuch an algorithm for a sector comprising multiple cells may beimproved, or at least will help to fully optimize any roll-out planningfor upgrading the mobile network, because the proposed cell-basedmodelling may be more realistic for assessing how a new user would beactually served when such user would like to connect to the mobilenetwork at a given physical location. Therefore, there may be a need(e.g., by mobile network carriers) for roll-out planning that is alsocapable of considering these developments, in particular carrieraggregation, for use by mobile network carriers.

Accordingly, various example implementations in accordance with thepresent disclosure provide methods and systems for optimized roll-outplanning for mobile carriers supporting carrier aggregation and yieldingdata traffic predictions with improved accuracy. Further, optimizedroll-out planning in accordance with the present disclosure may supporta wider range of weighting parameters in order to better refinetechnical recommendations. In accordance with the present disclosure, anoptimized network roll-out plan for a mobile network may be provided,comprising sectors fitted with multiple cells, with each cell workingover its own respective carrier frequency. In this regard, in a mobilecellular network, a physical location divided into one or more sectorsis usually referred to as a site. For example, a site may typically bedivided into 3 sectors, each spanning over 120°, in order to provideisotropic service capabilities. Each sector is then defined as aphysical area around a site, which is covered by one or more cells(e.g., network elements comprising suitable circuitry for serving asector using a particular carrier and technology through one or moreantenna elements).

The optimized network roll-out plan may be provided by gathering datafrom several network counters, and then analyzing the gathered data, todetermine available network resources for data traffic. The dataanalysis (step) may further comprise a congestion detection (substep)using a probability mass function (PMF) and a cumulative distributionfunction (CDF) modelling in order to assess what the achievable datathroughput for a user present in the sector could be. The congestiondetection substep may involve at least one carrier aggregation (CA)scenario between two or more cells in the sector, with congestionsituation being detected by comparing a predefined percentile of anaggregated CDF curve, weighted based on probabilities of possible cellconnection configurations, with a predefined minimal throughput level.

Thus, example implementations in accordance with present disclosure maybe advantageous in comparison to existing solutions because they includea broader range of modelling scenarios, such as for assessing how userspresent at a physical location are served, on a sector-by-sector basis,through an aggregated metric. As a result, data throughput for userspresent at a physical location may be defined with better precision, asthe capacity adjustments are forecast in a coordinated way, while thefine tuning of each cell is still performed individually.

In an example implementation, pain points may be identified on acell-by-cell basis, such as depending on measured load-balancingfeatures. This may make it possible to target improvements in a modularway for each sector when carrying out the roll-out planning.

In an example implementation, pain points identified on the networkbased on load-balancing features between all available cells may belabelled as coverage or capacity issue. This may make it possible toprune all available technical solutions to only a relevant subsetthereof and hence to significantly ease the overall processing whencarrying out the roll-out planning.

In an example implementation, classes of services corresponding to userprofiles may be defined based on subscription types, and an achievablethroughput is computed for each user profile on each sector. Thus, thenetwork dimensioning may, as a result, perfectly match the trafficconstraints according to the patterns derived on a profile-by-profilebasis.

In an example implementation, optimized roll-out planning may compriseperforming a prioritization assessment (or step), such as depending on asector value set by the mobile carrier as well as on the pain pointseverity. This prioritization may further help refine and optimize theroll-out planning.

In an example implementation, optimized roll-out planning furtherinvolves a cost estimation step, including both capital and operationalexpenditures. This cost estimation step can be performed after theprioritization step in order to validate the prescribed recommendationsin view of predefined budget constraints, so that the overall total costof ownership of the network is minimized in parallel.

In an example implementation, optimized roll-out planning may compriseuse of a traffic growth prediction scheme, such as to fine-tunetimelines for roll-out. When combined with a class-of-servicedifferentiation scheme (e.g., depending on new subscription expectationsby type), the timelines for roll-out then may also be adjusted dependingon the distribution of the overall traffic growth with respect to userprofile. Accordingly, the optimized roll-out planning efficiently usescounters of a mobile network in order to make recommendations on where,when, and which technology should be used in order to maximize thecapacity of the monitored mobile network at the lowest possible cost.

Further advantageous features aimed at optimizing the roll-out of amobile network are discussed below, with reference to the drawingsillustrating example implementations for the present disclosure. It willbe understood that the advantageous features pertaining to each of theseexample implementations can be taken alone or in combination forimplementing the present disclosure.

FIG. 1 illustrates a sequence of steps of the proposed scheme foroptimized roll-out planning according to an example implementation forthe present disclosure. Shown in FIG. 1 is an example sequence of steps(A) to (F) for implementing optimized roll-out planning scheme, yieldingan improved roll-out plan 101 for a mobile network 100. In this regard,the mobile network 100 comprises a cellular mobile network 100.

In this regard, the various steps described hereinafter may be performedin a system (not shown) that comprises suitable circuitry forimplementing various aspects of the present disclosure. Such circuitrymay comprise, e.g., general or dedicated processing circuitry, storagecircuitry, communication-related circuitry, etc. In some instance, suchsystem may be implemented as a single physical apparatus, which mayreside centrally in a network utilizing the described scheme, and beoperated by the network provider. In other instances, however, thevarious steps and/or related operations may be performed by variousdifferent components and/or subsystems of the network. In this regard,the different components and/or subsystems may interact with each other,and data or control services may be performed or handled either in acentralized way, or may have their functionalities distributed among thedifferent subsystems, for example leveraging the cooperation between theelements of each subsystem.

The roll-out planning optimization scheme described hereinafter may befor a site implemented based on LTE (Long Term Evolution; a 4G mobilestandard) technology and two cells. The two cells may be configured, forexample, at respective carrier frequencies of 800 MHz and 1800 MHz, andhaving a bandwidth of 10 MHz and 20 MHz, respectively, centered overtheir carrier center frequency. Nonetheless, it should be understoodthat this specific use scenario and all the features pertaining to it,particularly with respect to the use of roll-out planning optimizationscheme described herein, are given by way of example only, and shouldnot be construed in a limiting manner. Rather, it should be appreciatedthat many of the details and features described herein (e.g., theformulas used by the proposed algorithm) may be extended to other usescenarios—e.g., sectors comprising more than two cells.

For example, the roll-out planning optimization described herein maysimilarly be extended to an additional (third) cell working at a carrierfrequency of 2100 MHz, and could also be further extended to a fourthcell working at a carrier frequency of 2600 MHz, and so on, wherebycarrier aggregation may then be performed on any of the availablecarrier frequencies, irrespective of the bandwidth available on eachcarrier frequency. Further, the center frequencies, which are given byway of example for LTE based implementation, are purely exemplary, andthe proposed algorithm would similarly work with any other centerfrequencies.

In the example use scenario shown in FIG. 1 , each sector of the mobilenetwork 100 may periodically send a set of counter data to its OperationSupport System (OSS). An example embodiment for optimized roll-outplanning uses a subset of these counters supplying input parameters in afirst data gathering step (A), and then processes them in a subsequentdata analysis step (B) in order to assess what is the achievablethroughput of users, on a sector-by-sector basis. In a first congestiondetection substep (B1), it determines whether enough throughput would beavailable for worst-served users, according to the carrier's qualitystandards; then, in a further pain point identification substep (B2),pinpoints the cell that is concerned in the first place, wheneverpossible, and eventually, in a third so-called pain point classificationsubstep (B3), the kind of problem occurring for this identifiedcell—e.g., whether it pertains primarily to capacity or coverage, isauxiliary set. When such a labelling as a coverage or a capacity issueis possible, the subsequent action recommendation step (D) is greatlysimplified because non-relevant technical options are already pruned asan output of this analysis step (B).

For example, possible actions that may be taken when congestion isdetected may comprise one or more of: 1) installing a new outdoor macrosite in the area; 2) adding a new cell to the given sector; 3) expandingthe bandwidth on a given cell; 4) adding antenna elements; 5) installinga new indoor small cell in the area; 6) installing a new outdoor smallcell in the area; and 7) performing load-balancing adjustment betweenexisting cells in the given sector.

Among all of these proposed technical options, some are essentiallydirected to coverage (e.g., the installation of a new indoor small cell)while others are essentially directed to capacity (e.g., adding a newcell on an existing sector), and therefore solutions solving one type ofproblem (e.g., labelled as coverage or capacity) can simply be ignoredwhen it is sought to solve an issue of another type. Thus, thesubsequent prioritization step (E), depending on a sector value (e.g.,train stations, where a lot of traffic is usually generated, and othersimilar critical places are very important for the reputation of mobilecarriers in terms of quality of service) and/or the severity of theproblem, depending on the level of scarcity of resources that has beenestimated, may be also simplified.

The optimal roll-out plan 101 may be then determined by taking intoaccount budget constraints in terms of capital expenditures (also knownas CAPEX, involving the acquisition of new network material, such asantennas, etc.) and operational expenditures (also known as OPEX, mainlydirected to manpower and maintenance costs) in a last so-called costestimation step (F). The budget is then divided up, starting from thetop of the list where the most critical and urgent actions are to befound, downwards, until the budget is exhausted.

Another aspect that may also be considered for optimizing the roll-outplan (101) is the setting and adjustment of deadlines. In this regard,data provided by the network counters may typically correspond topresent conditions, and therefore may not provide any historicalperspective on the evolution of traffic, or any extrapolation scheme inorder to forecast how the network traffic will evolve on the short andmid-term. Thus, timelines may be addressed in the growth prediction step(C), whose function is to extrapolate the network data over futureperiod(s) of time (e.g., over the next 6, 12 and 24 months). The dataanalysis step (B), and all of its substeps (B1, B2, B3), may then bereiterated (re-performed), which allows deadlines to be set for theroll-out with a fine granularity, along with the best possible technicaloptions at each stage. This (the reiteration of the data analysis step(B)) is illustrated in FIG. 1 as a feedback loop from the growthprediction step (C) back to the data analysis step (B). Examplespertaining to the growth prediction scheme modelling are detailed below,in order to further optimize the roll-out planning.

More details are now provided for the various steps in the sequence. Inthis regard, reference is made hereinafter resource elements (REs),which correspond to a defined amount of time-frequency resources, aswell as to physical resource blocks (PRBs), which correspond to aminimum allocable resource to a user, composed of a set of REs, and tochannel quality indicators (CQIs), which indicate the radio channelquality between the user and a given cell, by indicating the number ofbits that can be sent per RE. The transmission time interval (TTI), setto 1 ms in LTE, corresponds to the minimum scheduling time for one user.The formulas defined below are meant to implement the data analysis step(B) according to an example implementation for the roll-out planningprocess, where the carrier aggregation (CA) technique, in which the usercan combine the resources of two or more cells, is now taken intoaccount for throughput estimation.

In the example use scenario shown in FIG. 1 , eight different networkcounters are used in order to provide data that will serve as inputparameters for the algorithm: a first counter c1, counting the dailynumber of total available PRBs per TTI in a cell; a second counter c2,counting the daily number of total users in that cell; a third counterc3, counting the daily number of active users in the cell; a fourthcounter c4, counting the daily number of used PRBs for VoLTE, e.g.,voice over LTE; a fifth counter c5, counting average daily distributionof CQI; a sixth counter c6, counting the daily number of users thatsupport CA and a certain number of secondary cells; a seventh counterc7, counting the daily number of users that are configured with acertain number of secondary cells among the ones supporting CA; and aneight counter c8, counting the daily number of TT's in which voice ordata were required to be sent.

The algorithm used during the data analysis step, for roll-out planningin accordance with the present disclosure, may use as inputs thefollowing parameters:

RE_(PRB), which is the number of REs in one PRB. This parameter is setby the technology that is used;Total PRBs per TTI, which is the total number of available PRBs in acell for one TTI, which is simply a function of the cell's bandwidth(e.g., 50PRBs/TTI for 10 MHz, 100PRBs/TTI for 20 MHz). This value isreturned by the first counter c1;VoLTE PRBs, which is the number of PRBs in a cell that are used forVoLTE traffic. This input value is returned by the fourth counter c4,e.g., with the help of QCIs (Quality of Service (QoS) classidentifiers). In this regard, usually voice traffic is heavilyprioritized on a network since it is very sensitive to delay and jitter,and therefore assigned the highest possible priority (e.g., QCI=1). As aresult, the overall traffic tagged with this class of service ismeasured, and more generally speaking, all possible classes of servicespertaining to voice traffic (e.g., typically QCI 1 and 5);Total UEs, which is the total number of users in a cell. This inputvalue is returned by the second counter c2;Active TTIs, which is the total number of TTIs in which data or voicewas actually sent during the observation period—typically daily—hence,disregarding the ones that ended up unused. This input value is returnedby the eight counter c8;Active UEs per TTI, which is the average number of active users in acell for one TTI, “active” meaning that data is in the process of beingsent or is waiting to be sent to such users. This input value isobtained by dividing the value returned by the third counter c3 by theone returned by eight counter c8;CA non-capable UEs, which is the number of users in a cell that do notsupport CA and can therefore only be connected to one cell at a time;CA capable UEs, which is the number of users in a cell that do supportCA, with at least two cells as in the present example, but possibly withmore cells (3, 4, . . . ). This input value is returned by the sixthcounter c6;CA capable and configured UEs, which is a subset of the number of usersin a cell that support CA. It represents the numbers of users in thecell that are not only capable, but also configured, to use CA. Thisinput value is returned by the seventh counter c7;CA capable not configured UEs, which is this input value is yielded byboth previous counters c6 and c7 as the number of users in a cell thatare actually CA capable, but are not configured to use CA;Thr_(RE), which is the achievable throughput in bits for one RE, as afunction of the CQI values (e.g., 16 for LTE), which is Thr_(RE)(q), ∀ q∈ possible CQIs; andProbability Mass Function (PMF) of the CQIs in a cell (PMF_(CQI)). Inthis regard, PMF_(CQI)(q)=Pr(CQI=q), ∀ q ∈ possible CQIs. These inputvalues are derived from the available data provided by the fifth counterc5.

In order to assess how users of a given cell C_(i) are served, first thenumber of available PRBs for data per TTI in that cell is calculated, byexcluding the voice traffic, as explained in the formula (1) below:

$\begin{matrix}{{{Available}{data}{PRBs}{per}{{TTI}\left( C_{i} \right)}} = {{{Total}{PRBs}{per}{{TTI}\left( C_{i} \right)}} - \frac{Vo{LTE}{PRB}{s\left( C_{i} \right)}}{{Active}{TTIs}}}} & (1)\end{matrix}$

Then, the number of PRBs per TTI in the cell C_(i) that a user currentlypresent in cell C_(i) can expect to use for data is computed as in theformula (2) below:

$\begin{matrix}{{{Available}{data}{PRBs}{per}{TTI}{for}a{{user}{}\left( C_{i} \right)}} = \frac{{Available}{data}{PRBs}{per}{{TTI}\left( C_{i} \right)}}{{Active}{UEs}{per}{TTI}\left( C_{i} \right)}} & (2)\end{matrix}$

And finally, the PMF of the achievable throughput for users present inthe sector using cell C=C_(i) may be derived, based on the value yieldedby the formula (2), using the formula (3) below.

PMF _(Thr,c) _(i) (t)=Pr(Thr=t)=Pr(CQI=q)  (3)

where:

t∈{t(q,C _(i)),∀q∈possible CQIs}

t(q,C _(i))=(1−Rt)*Thr _(RE)(q)*RE_(PRB)*Available data PRBs per TTI fora user(C ₁)*Number of TTIs

where Rt corresponds to a correction factor due to the fact that not allbits sent over the air are received correctly. Thus Rt corresponds tothe block error rate (BLER) rate and is usually estimated around 10%.

In order to provide a more realistic estimation of the averagethroughput achieved by users present on a site whose sectors are fittedwith multiple cells covering the same areas with different carriers, andnot only a single cell C_(i), example implementations according to thepresent disclosure incorporate carrier aggregation (CA) in aprobabilistic modelling. For example, In the case of a sector with 2cells C_(i) and C_(j), working respectively on a first carrier frequencyL1 and a second carrier frequency L2, there are three possible cellconnection configurations for a user: (i) the user is connected to cellC_(i) only; (ii) the user is connected to cell C₁ only; and (iii) theuser is connected to both C_(i) and C_(j), using CA.

The probability of a user in the sector being connected to cell C_(i)only is given by the formula (4) below, corresponding to configurationscenario (i)

$\begin{matrix}{{P{r\left( {{Conf} = C_{i}} \right)}} = \frac{\begin{matrix}{{{CA}{non}{capable}{UEs}\left( C_{i} \right)} +} \\{{CA}{capable}{not}{configured}{UEs}\left( C_{j} \right)}\end{matrix}}{{Total}{{UEs}\left( C_{i} \right)}}} & (4)\end{matrix}$

The formula (4) would apply similarly when the user is connected to cellC_(j), corresponding to configuration scenario (ii).

As for as configuration scenario (iii), the probability of a user in thesector being connected to C_(i)+C_(j) is given by the formula (5) below:

$\begin{matrix}{{\Pr\left( {{Conf} = {C_{i} + C_{j}}} \right)} = \frac{\begin{matrix}{{{CA}{capable}{and}{configured}{UEs}\left( C_{i} \right)} +} \\{{CA}{capable}{and}{configured}{UEs}\left( C_{j} \right)}\end{matrix}}{{{Total}{{UEs}\left( C_{i} \right)}} + {{Total}{{UEs}\left( C_{j} \right)}}}} & (5)\end{matrix}$

Further, the PMF of the achievable throughput for users in the sectorusing both cells C_(i), C_(j) is given by the formula (6) below:

PMF _(Thr,C) _(i) _(+C) _(j) (t)=Σ_((t) _(i) _(,t) _(j) _()∈{(t) _(i)_(,t) _(j) _(=t}) PMF _(Thr,C) _(i) (t _(i))*PMF _(Thr,C) _(j) (t_(j))  (6)

where:

t _(i) ∈{t(q,C _(i)),∀q∈possible CQ1s}

t _(j) ∈{t(q,C _(j)),∀q∈possible CQ1s}

The PMF of the achieved throughput for users in the sector correspondsto an aggregation of all the 3 possible configurations, hence providinga more accurate and realistic modelling according to the formula (7)below, which is actually a modified version of the formula (3) mentionedhere above, now taking CA into account and the probability of eachscenario yielded by previous the formulas (4) and (5) as weights w.

PMF _(Thr,aggregated)(t)=Σ_(C∈{C) _(i) _(,C) _(j) _(,C) _(i) _(+c) _(j)_(}) Pr(Conf=C)*PMF _(Thr,C)(t)  (7)

where:

t∈{t _(i) +t _(j)}

t _(i) ∈{t(q,C _(j)),∀q∈possible CQ1s}

t _(j) ∈{t(q,C _(j)),∀q∈possible CQ1s}

The throughput for some of the worst-served users in the sector is thendefined as a low percentile Pe of the PMF of the achievable throughputof all users in the sector, and recommendations are made accordingly ifthe values obtained are situated below a predefined minimal throughputlevel, Tm, determining whether congestion occurs or not, as explained inmore details below with respect to FIG. 2 a.

In the example implementation currently described, this low percentilePe may be set to, e.g., 10% (or 0.1) as shown in the formula (9) below.

$\begin{matrix}{{Thr_{{worst}\_{UEs}}} = {\left. x \middle| {\sum_{t = 0}^{x}{PM{F_{{Thr},{aggregated}}(t)}}} \right. = 0.1}} & (9)\end{matrix}$

The Cumulative Distribution Function (CDF) of any of the PMF throughputsdefined in the formula (9) above is defined by the formula (10) below:

$\begin{matrix}{{CD{F_{Thr}(x)}} = {\sum_{t = 0}^{x}{PM{F_{Thr}(t)}}}} & (10)\end{matrix}$

Thus, the formula (9) can be reformulated as the formula (11) below:

Thr _(worst_UEs) =X\CDF _(Thr,aggregated)(X)=0.1  (11)

FIG. 2 a illustrates cumulative distribution function curves derivedfrom network counters for each cell and the combination thereof in acarrier aggregation scenario with two cells, as well as the aggregatedcumulative distribution function curve used for throughput thresholdcomparison and traffic congestion analysis, in accordance with anexample implementation of the present disclosure.

Shown in FIG. 2 a are CDF curves for expected throughputs computedaccording to the formulas described above. In particular, a first curvef1 is depicted for a first cell C_(i) working on a first carrierfrequency L1 (e.g., typically 800 MHz for LTE), a second curve f2 isdepicted for a second cell C₁ working on a second carrier frequency L2(e.g., typically 1800 MHz for LTE), a third curve f3 corresponds tocarrier aggregation between both C_(i) and C₁, and a fourth curve g,representing a aggregated curve obtained by weighting of the first threecurves by applying the formula (7) and the weights w of the formulas (4)and (5).

By applying the formula (11) to the aggregated curve g, the low-endthroughput may be obtained simply by detecting when a horizontal linecorresponding to a CDF=0.1 value crosses this aggregated curve g. Thispoint is materialized by a bold black bullet, yielding, according to thedepicted example, a throughput of 9.33 Mbps. In order to interpret thisvalue, it may be necessary to compare it with the predetermined minimalthroughput level Tm, which is set, according to the particularillustrated example implementation, to 10 Mbps. Such a threshold valueis considered to be reasonable for downloading data, and even for videostreaming.

A congestion situation Cs can then be determined (e.g., being easilyvisualized) by comparing the obtained low-end throughput value with thisminimal throughput level Tm: if Thr<Tm, then congestion is detected.Thus, as shown in FIG. 2 a, congestion indeed occurs, because theyielded low-end throughput value of 9.33 Mbps is below the presetboundary of 10 Mbps.

According to other alternative implementations, the minimal throughputlevel Tm may be set to other threshold values, and the percentile alsoset to other ratios; it can be appreciated though that the lower thepercentile value is chosen, together with the higher the value at whichthe minimal throughput level Tm is set, the better service quality isensured.

As shown in the example illustrated in FIG. 2 a , the other throughputsfor all other CDF curves f1, f2, and f3, indicated by crosses instead ofa bullet, read respectively 2.93 Mbps, 8.84 Mbps, and 18.09 Mbps for aCDF value of 0.1, but yet do not have any meaning corresponding to astatistical modelling of the reality, unlike the one of 9.33 Mbps of theaggregated curve g, which corresponds to a low-end service scenario;these throughput values merely indicate what low-end throughput couldhave been reached for each of the three possible cell connectionconfigurations, described above, taken independently.

The detection of a congestion situation Cs, by comparing the yieldedlow-end throughput value (e.g., 9.33 Mbps, as indicated by the boldbullet point shown in FIG. 2 a ), with the minimal throughput level Tm,can be considered as the first congestion detection substep (B1) of thedata analysis step (B) illustrated previously in FIG. 1 . Such acongestion situation Cs happens when the computed bold bullet pointfinds itself in the dashed zone located left of a vertical linecorresponding to the minimal throughput level Tm depicted in FIG. 2 a .Depending on the difference between the obtained low-end throughput ofthe aggregated curve g and the minimal throughput level Tm, or a ratiobetween these two values, a congestion level can also be determined,which may be helpful to prioritize actions to take in a subsequent step,such as the prioritization step (E) illustrated in FIG. 1 .

In the example illustrated in FIG. 2 a , the throughput of 9.33 is closeto 10 Mbps, so that the congestion level is not dramatic. Should, incontrast, a throughput of 5 Mbps be returned by the calculationaccording to the proposed scheme, the congestion level may be consideredsevere.

In accordance with an example implementation of the roll-out planningscheme described herein, the next substep of the data analysis step (B)is the pain point identification substep (B2), which is explainedfurther in view of FIG. 2 b and the following the formulas (12), (13)and (14) below.

FIG. 2 b illustrates the same cumulative distribution functions as onprevious FIG. 2 a , but now with an emphasis on the contribution of eachcell for load-balancing analysis, in accordance with an exampleimplementation of the present disclosure.

For example, the probability of 0.1 related to the Thr_(worst_UEs) isthe sum of 3 contributions, one for each of the user configurations:

0.1=Σ_(C∈{C) _(i) _(,C) _(j) _(,C) _(i) _(+C) _(j) _(})contrib_(c)  (12)

where each contribution is defined as follows:

contrib_(c) _(i) =Pr(Conf=C _(i))*CDF _(Thr,C) _(i) (Thr _(worst_UEs))

contrib_(c) _(j) =Pr(Conf=C _(j))*CDF _(Thr,C) _(j) (Thr _(worst_UEs))

contrib_(c) _(i) _(+c) _(j) =Pr(Conf=C _(i) +C _(j))*CDF _(Thr,c) _(i)_(+c) _(j) (Thr _(worst_UEs))  (13)

The bold bullet point of aggregated curve g having a CDF of 0.1, asshown in FIG. 2 a and FIG. 2 b , is actually obtained by applying theweights w yielded by previous the formulas (4) and (5) to curves f1, f2,and f3. The crosses that are visible in FIG. 2 b indicate the CDFsvalues that are used by the formula (13). Then, a ratio r of thecontributions of the 2 cells is defined according to the formula (14)below:

$\begin{matrix}{r = \frac{{contrib}_{C_{i}}}{{contrib}_{C_{j}}}} & (14)\end{matrix}$

The pain point PP is determined according to the formula (15) below:

if r>b—>PP=C _(i)

if r<1/b→PP=C _(j)

else→PP=balanced  (15)

where the b parameter corresponds to a balancing ratio, here chosen tobe equal to 1.25.

In other words, if the ratio of contribution of the two cells C_(i) andC₁ is comprised within 0.8 to 1.25, e.g., close to 1, it is consideredthat both cells are equally affected by the congestion problem.Otherwise, it is possible to pinpoint which one of the two cells, C_(i)and C₁, is most affected. In other words, the pain point identificationsubstep (B2) possibly indicates whether the congestion problem isbalanced between cells, and otherwise, indicates which cell is mostconcerned. Therefore, as an outcome of this pain point identificationsubstep (B2), it may already be possible to focus, at this stage, onmeasures to be taken for specific cells of each sector analysed duringthe subsequent action recommendation step (D).

If a specific cell can be identified during the pain pointidentification substep (B2), then a further advantageous classificationsubstep (B3) of the data analysis step (B) is proposed according to theexample implementation described, in order to classify these pain points(PP) into subcategories, and further prune relevant technical options tosolve the congestion issue to be considered during the subsequent actionrecommendation step (D).

In particular, if a paint point PP is not balanced and thus a given cell(e.g., cell C₁) is concerned, the pain point classification substep (B3)determines whether the issue detected pertains to coverage or capacityaccording to the formula (16) below:

if Pr(bad_(coverage) ,PP)>P _(cov) →PP−cov

else→PP−cap  (16)

wherein the probability of a user in a cell to be indoor or at the celledge is defined according to the formula (17) below:

$\begin{matrix}{{\Pr\left( {{{bad}\_{coverage}},C_{i}} \right)} = \frac{{CA}{capable}{not}{configured}{{UEs}\left( C_{i} \right)}}{{CA}{capable}{{UEs}\left( C_{i} \right)}}} & (17)\end{matrix}$

In the formula (17) above, the probability of a bad coverage isestimated by measuring the ratio between the number of users that couldhave potentially used CA on the concerned cell, but actually did notbecause of their radio conditions. This ratio is compared, in theformula (16), with the Pcov boundary percentage (e.g., set to around20%), in order to determine whether coverage (cov) or capacity (cap) isthe most critical issue for the concerned pain point.

As a result, the paint point classification substep (B3) allows thelabelling of the pain points (PP) as coverage or capacity problems bystill involving carrier aggregation (CA) as a modelling parameter. Theadditional pruning of available technical options further simplifies theprocessing of the subsequent recommendation step (D).

In other example implementations, the input parameter b (balancingratio) used in pain point identification substep (B2) and Pcov (boundaryprobability of a bad coverage) used in paint point classificationsubstep (B3) could be adjusted to different levels, depending on how thefiltering of the technical options is sought to be performed. The closerto 1 the balancing ratio b is set, the easier it becomes to pinpointindividual cells, and perform subsequent classification thereof; then,the higher the boundary probability of a bad coverage Pcov is set, themore the technical recommendations will be oriented toward capacitysolving.

As mentioned above, despite the fact that the detailed example given forthe data analysis step (B) further in view of FIGS. 2 a and 2 b involves2 cells working on two carrier frequencies only, the disclosed schemecould also be extended to scenarios involving additional cells workingon other respective carriers (e.g., with a third cell C_(k) working on athird carrier frequency L3, such as typically 2100 MHz for LTE, and afourth cell C_(l) working on a fourth carrier frequency L4, such astypically 2600 MHz for LTE, and so on) using the same principles of CA.

For example, assuming that the multiplexing of a random number ofcarriers simultaneously is possible for a given technology supporting CA(e.g., also more than two at the same time), the possible cellconnection configurations for a user in sector, having, for example,three cells using such a technology, would then be: (i) User connectedto cell1 C_(i); (ii) User connected to cell2 C₁; (iii) User connected tocell3 C_(k); (iv) User connected to cell1+cell2 C_(i)+C_(j); (v) Userconnected to cell1+cell3 C_(i)+C_(k); (vi) User connected to cell2+cell3C₁+C_(k); and (vii) User connected to cell1+cell2+cell3C_(i)+C_(j)+C_(k).

The aggregated CDF curve may then be obtained by combining all of theseCDFs with the corresponding weights of the probability of aconfiguration being one of these cell connection configurations (e.g.,Pr(Conf=(i)-(vii))), by taking further inputs from additional networkcounters and adjusting the concerned the formulas (e.g., the formulas(4) and (5)). The congestion detection then may be similarly obtained bycomparing whether the chosen percentile (e.g., 0.1) of the CDFaggregated curve is below the predefined minimal throughput level Tm(e.g., 10 Mbps).

Then, after having carried out this first congestion detection substep(B1), the trigger points for the pain point identification would besimilarly set by calculating also the contributions of each cell aspreviously according to the formulas (12), (13), and (14). Then, insteadof only one ratio r, two ratios are calculated, a first ratio betweenthe contributions of cell1 and cell2 (C_(i) and C₁), and a second ratiobetween the contributions of cell1 and cell3 (C_(i) and C_(k)). Then, weuse again the same thresholds 1.25 and 0.8 (e.g., by applying the samevalues of balancing ratio b) in order to identify the trigger point. Thepain point (PP) configuration may be done as follows:

-   -   one cell only is identified: when one cell's contribution is        higher than the other 2. In this case, a subsequent        classification substep (B3) can be further carried out in order        to determine whether this identified cell is concerned primarily        by a coverage or a capacity problem;    -   two cells are identified: when two cells' contributions are        higher than the third but balanced between them; in that case,        two further computations are performed to simplify the        decision-making process; and    -   a balanced situation when the contribution of all three cells is        similar, in which case no further computation steps are        undertaken.

The same principles and the formula adjustments would be made for afour-carrier scenario, or more, for which, during the data analysis step(B), the same sequence of substeps (B1, B2, B3) would apply, the painpoint classification step (B3) remaining subordinated to the outcome ofthe previous pain point identification substep (B2).

In some example implementations, it would also be possible to consider,within the framework of the optimized roll-out planning in accordancewith the present disclosure, subscription type of users in order toseparate data flows and make distinct computations for each category ofusers. In this regard, mobile carriers may offer various levels ofmaximum data throughputs depending on a subscription type (e.g., maximumof 1 Mbps for a small package “S”, 10 Mbps for a medium package “M”, and30 Mbps for a large package “L”). Hench, it may be unrealistic or evenunfair to consider that all network resources should be equallydistributed among all users, irrespective of their subscriptioncategory. Accordingly, roll-out planning may be configured to accountfor the various subscription types, such as by mapping subscriptioncategories to various classes of services in order to provide adifferentiated network resources allocation scheme depending on thesecategories.

For example, using LTE technology, voice-related traffic is alwaysmarked with QCI classes 1 and 5 irrespective of the QCI data class ofthe user. In the above example calculations, voice-related traffic wasremoved from the available resources of the carrier before calculatingthe data throughput curves; then, the remaining resources (e.g., theresources available for data traffic) were divided equally among theactive users, independently from any QCI data class (as in the formula(1) described above). As a result, only one aggregated throughput curvewas yielded per carrier.

This is illustrated in FIG. 1 , which shows arrows labelled “S”, “M”,and “L” to indicate the possibility to separate data flows for eachsubscription type, by calculating one aggregated throughput distributionper QCI data class (e.g., class 9 mapped to subscription type “S”, class8 mapped to subscription type “M”, and class 7 mapped to subscriptiontype “L”) so as to produce one low-end throughput value per each QCIdata class. This way, the resources of the mobile carrier would beallocated to each QCI data class taking into consideration theirpriorities, making the provided statistical modelling for assessingexpected throughput for a user in the sector even more realistic.

It may even be appreciated that such traffic modelling involving QoSclass segmentation may be applied also for a single cell takenindependently and not necessarily for a sector having multiple cells,since this QoS segmentation is not subordinated to the use of any CAtechnology. Such a modelling taking into account subscription types isfurther advantageous for additional reasons, such as, for example, itmakes it possible to adjust also the minimal throughput level Tmdepending on each data class in order to further fine-tune the roll-outplanning.

Another advantage of considering subscription types when computingexpected low-end throughputs is that it allows the calculations to beimproved for growth prediction, and therefore also allows a betterfine-tuning of the roll-out plan, especially concerning the timelines.In this regard, the number of users per subscription type maydramatically vary, so that a weighting according to the critical massthereof may be highly relevant (e.g., usually the best sold subscriptiontype is, by far, the medium “M”) to extrapolate traffic growthpredictions, on top of historical counter data that may be used toestimate trends—e.g., a linear growing rate of the number of activeusers at a rate X/week over a fixed time sliding window, such as thelast 6 months or 12 months.

The prediction of future low-end throughput values in step (C)illustrated in FIG. 1 may also be based on predictions of future countervalues (e.g., c1 to c8), while the prediction of future values of eachcounter is based on historical (past) values of that counter, from whicha trend is derived. It is important to note that the analysis ofhistorical values of counters can yield trends that are not necessarilylinear: they could also be exponential, logarithmic, etc.

Moreover, historical values of a counter may include disruptions. Suchdisruptions can cause the estimation of the trend to be imprecise, or inextreme cases completely wrong. In this regard, there are two types ofdisruptions: temporary and permanent. Temporary disruptions are rareevents that last for a limited period of time, during which the countervalue may be exceptional and uncharacteristic. Examples of temporarydisruptions include times corresponding to exceptional usage (e.g.,Christmas to New Year's period, stretching over about two weeks; summerholidays, which usually spread over July and August) and/or events orconditions in the network that are of temporary nature (e.g., failure ofa network element, which may lasting for a very limited period of time,such as an hour). In contrast, permanent disruptions are fundamentalchanges in the network configuration that happen at a point in time andremain in that new state. Examples of permanent disruptions include thedeployment of a new network element in the area, or the opening of a newshopping mall.

Both of these types of disruptions in the historical counter values cannegatively affect the prediction step (C). Therefore, in an exampleimplementation, the prediction step (C) first detects, and thenclassifies, the disruption as being of one type or another, and thenprocesses these disruptions in order to remove the effect thereof,thereby making the prediction results more precise.

In another example implementation, the processing of temporarydisruptions may comprise having this type of disruption be partially orfully disregarded for prediction step (C). With partial disregarding,the data samples that are considered to be affected by the temporarydisruption may be assigned a lower importance, and therefore weightedaccordingly (e.g., by multiplying with a coefficient 0.2 instead of 1),while all the other samples having normal importance are not weighted.With full disregarding, data samples affected by the temporarydisruption may be completely ignored, such as by setting the weightingcoefficient to be equal to 0.

In an example embodiment, permanent disruptions are not disregarded, andfurther set time boundaries to historical data analysis. Indeed, when apermanent disruption is detected in the historical values of a counter,the time when the permanent disruption happened preferably sets the timewindow from the present to an anterior time limit until which theanalysis can go backwards in time. Thus, the data analysis to estimatethe trend of the counter may take into consideration only the samplesafter the last permanent disruption, and yet not the ones before thatdisruption.

As far as the growth prediction step (C) is concerned, not only QCIclasses segmentation and the detection of disruptions may be useful, asexplained in the above paragraphs, but also the use of network equipmentconfiguration data to make traffic growth predictions more precise. Inthis regard, in view of the network equipment configuration data, it ispossible to determine the last time when a major configuration change(e.g., new carrier in the sector, new sector in the area, change intransmission power, etc.) occurred, and look back only since that majorchange as a starting point for trend estimation, hence making the datamuch more precise. These equipment configuration data may be part of thefurther inputs, which may be obtained from other sources (e.g., otherdata sources 102 in FIG. 1 , which may comprise network-generated anduser-generated events traces) in addition to the network counters.

In other example implementations, other relevant inputs supplied byother sources (e.g., from other data sources 102 in FIG. 1 ) may be alsoleveraged in order to further optimize the roll-out planning process, ontop of network equipment configuration data. In this regard, withoutevent traces, all results calculated are related to the entiregeographical area of a sector; hence, they do not make statements ordraw any conclusions about any particular part of a sector, only thesector as a whole.

For example, using the user-generated events traces, which are eventsreported by each individual user, the results may be localized with aspecial focus on particular geographical areas within the sector, e.g.,by application of trilateration algorithms. It would then be able toidentify different types of hotspots as sub-pain points within the areaof the sector, where additional small cells could be installed, forexample.

Although the example implementation of the described roll-out planningoptimization deals with a sector of an LTE cellular mobile network, aperson with ordinary skill in the art will understand that such roll-outplanning may also be applied to any mobile network technology supportingthe carrier aggregation technique, such other cellular networks, othercarrier frequencies, and/or other telecommunication solutions (e.g.,IEEE standards like WLAN, WiMAX, etc.)

It will also be understood that the network counters used could be setto provide inputs other than daily rates, especially in order to adjustthe network capacity to peak times (e.g., 15-minute or hourly rates maybe most appropriate).

Also, it should be understood that instead of considering an achievablethroughput of users present at a physical location, exampleimplementations according to the present disclosure, which providerealistic throughput estimations and congestion detection assessment ona whole sector, in order to support roll-out planning, may also beapplied to the calculation of a throughput for a new user entering thisvery same physical location without departing from the scope of thepresent disclosure. For example, minor changes may be made to theformulas (1) and (2) to account for how new users would likely be servedin view of existing resources available for data traffic, instead ofcomputing an average between all current users in the sector.

Other implementations of the disclosure may provide a non-transitorycomputer readable medium and/or storage medium, and/or a non-transitorymachine readable medium and/or storage medium, having stored thereon, amachine code and/or a computer program having at least one code sectionexecutable by a machine and/or a computer, thereby causing the machineand/or computer to perform the steps as described herein.

Accordingly, the present disclosure may be realized in hardware,software, or a combination of hardware and software. The presentdisclosure may be realized in a centralized fashion in at least onecomputer system, or in a distributed fashion where different units arespread across several interconnected computer systems. Any kind ofcomputer system or other apparatus adapted for carrying out the methodsdescribed herein is suited. A typical combination of hardware andsoftware may be a general-purpose computer system with a computerprogram that, when being loaded and executed, controls the computersystem such that it carries out the methods described herein.

The present disclosure may also be embedded in a computer programproduct, which comprises all the features enabling the implementation ofthe methods described herein, and which when loaded in a computer systemis able to carry out these methods. Computer program in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: a) conversion to anotherlanguage, code or notation; b) reproduction in a different materialform.

While the present disclosure makes reference to certain implementations,it will be understood by those skilled in the art that various changesmay be made and equivalents may be substituted without departing fromthe scope of the present disclosure. In addition, many modifications maybe made to adapt a particular situation or material to the teachings ofthe present disclosure without departing from its scope. Therefore, itis intended that the present disclosure not be limited to the particularimplementation disclosed, but that the present disclosure will includeall implementations falling within the scope of the appended claims.

List of References and Acronyms A Data gathering step B Data analysisstep B1 Congestion detection substep B2 Pain point identificationsubstep B3 Pain point classification substep C Growth prediction step DAction recommendation step E Prioritization step F Cost estimation stepb Balancing ratio c1 1^(st) network counter c2 2^(nd) network counter c33^(rd) network counter c4 4^(th) network counter c5 5^(th) networkcounter c6 6^(th) network counter c7 7^(th) network counter Ci Cellidentifier f1 First curve corresponding to a first carrier frequency f2Second curve corresponding to a second carrier frequency f3 Third curvecorresponding to both first & second carrier frequencies g Aggregatecurve L1 First carrier frequency L2 Second carrier frequency L3 Thirdcarrier frequency L4 Fourth carrier frequency L “Large” subscriptionpackage M “Medium” subscription package Tm Throughput minimal level(preset by carrier) Cs Congestion situation Pe Low percentile of PMFused for calculations Pcov Lower boundary for the probability of a badcoverage q CQI value Rt Block error rate S “Small” subscription packagew Weighting parameter 100 Mobile Network 101 Roll-out plan 102 Otherdata sources besides network counters CA Carrier aggregation CapCapacity CDF Cumulative distribution function Cov Coverage CQI Channelquality indicator LTE Long-term evolution (4G mobile technologystandard) PMF Probability mass function PP Pain point PRB Physicalresource block QCI Quality of Service (QoS) Class Identifier RE Resourceelement Thr Throughput TTI Transmission time interval UE User equipmentVoLTE Voice over LTE

What is claimed is:
 1. A method for managing a network that comprises aplurality of cells, the method comprising: obtaining network dataassociated with the network; analyzing the network data, wherein theanalyzing comprises analyzing throughput of users on a sector-by-sectorbasis in the network; applying, based on the analyzing of the networkdata, a growth prediction for the network; and optimizing, based on theapplying of the growth prediction, a network roll-out plan for use inthe network.
 2. The method according to claim 1, wherein applying thegrowth prediction comprises extrapolating the network data over one ormore future periods of time.
 3. The method according to claim 1, whereinapplying the growth prediction comprises detecting and classifyingdisruptions in historical values for at least one network relatedparameter, wherein the classifying of disruptions comprising identifyinga type of disturbance.
 4. The method according to claim 1, whereinanalyzing the network data comprises: applying congestion detectionbased on the network data; identifying based on the congestion detectionone or more points of interest associated with one or more of theplurality of cells; and assessing the one or more points of interest. 5.The method according to claim 4, wherein applying the congestiondetection comprises: assessing an aggregation scenario for a particularsector in the network, between two or more cells of the plurality ofcells; detecting for the aggregation scenario, a congestion situationbased on probabilities of one or more possible cell connectionconfigurations in the sector, and a predefined minimal throughput level;and assessing achievable data throughput for users in the sector.
 6. Themethod according to claim 5, wherein the sector further comprises atleast three cells, and further comprising assessing the aggregationscenario based on support of multiplexing of a random number of carrierfrequencies of the at least three cells.
 7. The method according toclaim 4, wherein applying the congestion detection comprises identifyingbased on the network data, one or more cells within a sector wherecongestion primarily occurs.
 8. The method according to claim 7, whereinthe identifying further comprises comparing at least one ratio ofcontribution between cells with a balancing ratio.
 9. The methodaccording to claim 7, further comprising, after identifying at least onecell within the sector, determining whether the identified at least onecell is primarily affected by a coverage or a capacity issue.
 10. Themethod according to claim 9, further comprising: computing a probabilityof a bad coverage; comparing the calculated probability of a badcoverage with a predefined lower boundary probability; and deriving acoverage or capacity issue labelling for the cell based on thecomparing.
 11. The method according to claim 1, further comprisingcomputing different data profiles according to different user profiles,wherein each user profile corresponds to a subscription type and eachsubscription type is mapped onto a specific class of service.
 12. Themethod according to claim 1, further comprising recommending one or moreactions based on the network data.
 13. The method according to claim 12,wherein the one or more actions comprise at least one of: installing anew outdoor macro site in the area, adding a new cell to the givensector, expanding the bandwidth on a given cell, adding antennaelements, installing a new indoor small cell in the area, installing anew outdoor small cell in the area, and performing load-balancingadjustment between existing cells in the given sector.
 14. The methodaccording to claim 12, wherein recommending the one or more actionscomprises generating a listing of technical options based on the networkdata.
 15. The method according to claim 1, further comprising generatingprioritization information and cost optimization information for use inthe optimizing of the network roll-out plan.
 16. The method according toclaim 15, further comprising generating the prioritization informationbased on a valuation scheme and/or a congestion level.
 17. The methodaccording to claim 1, further comprising determining or adjusting thenetwork roll-out plan based on network-generated and user-generatedevents traces in order to refine localization information wherecongestion occurs.
 18. The method according to claim 17, furthercomprising refining localization information for where congestion occursbased on the network-generated and user-generated events traces.
 19. Themethod according to claim 1, further comprising determining or adjustingthe network roll-out plan based on network equipment configuration data.20. The method according to claim 1, further comprising determining oradjusting the network roll-out plan based on historical network dataassociated with the network.
 21. The method according to claim 20,further comprising detecting disruptions based on the historical networkdata.
 22. The method according to claim 20, further comprising settingtime windows for trend estimations based on disruptions classified aspermanent.
 23. The method according to claim 20, further comprisingclassifying the disruptions as temporary or permanent, wherein temporarydisruptions are at least partially disregarded, and wherein permanentdisruptions set time windows for trend estimations.
 24. The methodaccording to claim 23, further comprising partially or fullydisregarding disruptions classified as temporary.
 25. The methodaccording to claim 1, further comprising determining available networkresources for data traffic based on the network data.